Non-linear data dependency detection in machine learning using hybrid quantum computing

ABSTRACT

Methods for detecting non-linear data dependencies in machine learning using hybrid quantum computing. Methods include receiving a selection of an accuracy metric. Methods include receiving a data set comprising a plurality of data elements for processing by a machine learning model operating on a machine learning system. Methods include identifying a plurality of data elements within each data set. Methods include identifying one or more features for each data element. Methods include determining a total number of features for the data set. Methods include reducing, by a quantum annealing method, based on the accuracy metric, the total number of features to a reduced number of features. Methods include inputting the reduced number of features into the machine learning model. Methods include outputting a result from the machine learning model.

FIELD OF TECHNOLOGY

Aspects of the disclosure relate to machine learning.

BACKGROUND OF THE DISCLOSURE

Machine learning systems are computers that utilize mathematical modelsto learn from both inputted data and experience. Typically, thislearning occurs without direct instruction. Machine learning systems mayimitate the way humans learn from information as well as the way humanslearn from personal experience.

Machine learning systems may use a machine learning model. The machinelearning model may be specifically designed to perform a predeterminedtask.

Machine learning models may classify data elements. In some machinelearning models, a data element may be broken down into its componentparts, also referred to herein as features. Upon feature identification,each identified feature as well as the group of classified features maybe linked to a classification.

Current trends have encouraged continuously increasing the amount ofdata inputted into developing machine learning models. Many speculatethat the robustness and developedness of the machine learning model isdirectly dependent on the quantity of the data absorbed by the model.Additionally, many also believe that the robustness and developedness ofa machine learning model depend on the level of granularity of thefeatures associated with an inputted data element.

However, it should be noted that the more data that is input into amodel, the more resources and time the system will need in order tocreate, improve and enhance a machine learning model. It should befurther noted that the more granularly the system reviews the inputteddata, the more resources and time the system will need in order tocreate, improve and enhance the machine learning model. Therefore, inorder to optimize a machine learning model and associated system, itwould be desirable to ensure that the data inputted into the system ispurely, or mostly, information that is advantageous to, and improves,the machine learning model and underlying system.

It should be noted that, for the purposes of this application, duplicatedata may be understood to mean two data element sets that include thesame, or very similar, information. An example of duplicate data mayinclude a first set of data elements that includes a first name, amiddle name and a last name and a second set of data elements thatincludes a full name. The first set of data elements may be consideredduplicative to the second set of data elements.

Also, for the purposes of this application, highly correlated data maybe understood to mean two data element sets that are closely related ortwo data element sets that are substantially, or completely, dependenton one another. An example of highly correlated data may include a firstset of data elements that corresponds to a height and a width of apredefined item, and a second set of data elements that corresponds to aweight of the predefined item. The height and the width of thepredefined item when compared to the weight of the predefined item maybe considered highly correlated.

When duplicate data or highly correlated data is received at a machinelearning system, the associated model may not learn, or may not learnsignificantly, from the inputted duplicate or highly correlated data.Therefore, both resources and time may be wasted on duplicate and highlycorrelated data inputted into, and processed at, a machine learningmodel.

As such, it would be desirable for systems and methods that reduce thequantity of data inputted into a machine learning model and associatedsystem by removing both the duplicate data and the highly correlateddata. The reduction of the inputted data may retain the accuracy of themachine learning system as well as reduce wasted resources and time.

It would be further desirable to utilize a quantum annealing algorithmrunning on either a quantum computer or a classical computer to reducethe number of features included in a set of data elements. When using aclassical computer, it would be further desirable to utilize a classicaloptimizer that is programed to execute a simulated quantum annealingalgorithm on a classical computing system.

Furthermore, it would be desirable to run the quantum annealingalgorithm to reduce the number of features identified within a set ofdata elements while ensuring that the accuracy level of the output isnot significantly compromised. It would be further desirable for theaccuracy level to be set by the user because the accuracy level may beproblem-dependent. As such, it would be further desirable for theaccuracy level to be set based on the identified possibility of a falsepositive output vis-à-vis the identified possibility of a false negativeoutput.

SUMMARY OF THE DISCLOSURE

Apparatus, methods and systems for detecting non-linear datadependencies in machine learning using hybrid quantum computing isprovided.

Statistical machine learning may depend on the availability of largevolumes of data to make a prediction. The data may be retrieved fromvarious sources. The data may be structured, such as in a spreadsheetfile, or unstructured, such as an image, source, video or portabledocument format (PDF) file. Many times, the data does not includecomplete files and, therefore, requires reformatting in order to be usedto build or create a machine learning model.

It should be noted that the larger the volume of data used to create amachine learning model, the better the model is in the quality of theprediction. However, larger quantities of data may require large amountsof resources and time to reformat the data in order to make the dataadvantageous for the machine learning model.

At times, the data may include duplicate data. Many times, when data isbeing retrieved from different sources, duplicate data may be retrieved.The duplicate data may not add value to the predictability of the model.Furthermore, the duplicate data may amplify the value of the duplicatedata thereby reducing the value of the singular data within the machinelearning model.

Also, the data may include correlated data. Correlated data may includedata which is intrinsically dependent on other data. For example, suchdata may include one data column which may result from a basic summationof a plurality of other data columns. As such, this correlated data maynot add value to the predictability of the model.

Removing both duplicate data and correlated data is an important itemwithin machine learning. There are several tools that detect linearcorrelation among data sets. Examples of tools may include singularvalue decomposition (SVD) and Pearson's co-variance (PCV).

However, currently there are no available efficient detection methodsthat can detect data dependencies when the data is not linearlycorrelated. Detection of non-linear dependencies may include a globaloptimization method that may be difficult to implement on a classicalcomputing system. Specifically, using a brute force search mechanism toidentify pairwise data dependencies and using entropy for non-linearitydetection are computationally expensive using a classical computer.

Therefore, it would be desirable to harness a hybrid quantum optimizeroperating on a hybrid quantum computer to detect and remove non-linearlycorrelated data.

A system and method for detection and removal of non-linearly correlateddata using hybrid quantum computing is provided. The system may includeand/or utilize a quantum optimizer.

A quantum optimizer, utilizing quantum optimization algorithms, may beused to solve optimization algorithms. Mathematical optimizationincludes identifying the best solution to a problem, according to apredefined set of criteria, from a set of possible solutions. Manytimes, an optimization problem may be formulated as a minimizationproblem, where one tries to minimize an error which depends on thesolution. As such, the optimal solution may be the solution thatcontains the minimal error.

An example of a quantum optimization algorithm may include a quantumapproximate optimization algorithm (QAOA). It should be noted that, forcombinatorial optimization, the QAOA may have a better approximationration that any known polynomial time classical algorithm.

An example of a quantum optimizer may be D-Wave produced by D-WaveSystems Inc. D-Wave utilizes quantum annealing methods. Quantumannealing is a process that starts from a quantum-mechanicalsuperposition of all possible states with equal weights. Possible statesmay also be referred to as candidate states. The system evolvesfollowing the time-dependent Schrödinger equation, which is a naturalquantum-mechanical evolution of physical systems. The amplitudes of allcandidate states keep changing, realizing a quantum parallelism,according to the time-dependent strength of the transverse field, whichcauses quantum tunneling between states.

If the rate of change of the transverse field is sufficiently slow, thesystem may stay close to the ground states of the instantaneousHamiltonian. Such a process may be referred to as adiabatic quantumcomputing.

If the rate of the change of the transverse field is accelerated, thesystem may leave the ground state temporarily but produce a higherlikelihood of concluding in the ground state of the final problemHamiltonian. Such a process may be referred to as diabatic quantumcomputing.

The transverse field may be finally switched off and the system may beexpected to reach the ground state of the classical Ising model. Theground state of the Ising model may correspond to the solution to theoriginal optimization problem.

A method that utilizes quantum annealing and/or a quantum optimizer forhyper parameter optimization is provided.

Additionally, it should be noted that, in classical computing methods,detection of dependency among parameters that are non-linearly relatedis often done by pair-wise comparison using massively parallel computingsuch as GPU. However, because of a partitioning problem with a GPU, aGPU can only optimize locally and an attempt to find a global optimalmay significantly slow down the parallelization.

Therefore, a hybrid computing method that utilizes a classical computeroperating with a graphical processing unit (GPU) and a quantum optimizerto build a model that efficiently performs hyper parameter optimizationis provided. Methods may include receiving, at a dependency detectionsubsystem, a set of input parameters and a set of current valuesassigned to each input parameter included in the set of inputparameters. The set of input parameters and the set of current valuesassigned to each input parameter may relate to a predeterminedclassification structure.

Methods may include transferring the set of input parameters and the setof current values assigned to each input parameter to the quantumoptimizer executing a quantum annealing method. Methods may includeidentifying, at the quantum optimizer, a set of hyperparameters includedin the set of input parameters.

Methods may include reducing, at the quantum optimizer, the set ofhyperparameters, using the quantum annealing method. Methods may includereturning the reduced set of hyperparameters from the quantum optimizerto the classical computer. Methods may include building, at theclassical computer operating with the GPU, a machine learning modelusing the reduced set of hyperparameters.

Methods may include using the machine learning model operating on theclassical computer to classify an unclassified data element within thepredetermined classification structure. An example of the unclassifieddata element may include an email. The predetermined classificationstructure may classify the email as being a valid email or a maliciousemail. Other examples of an unclassified data element may include amortgage application and an investment and securities within a mutualfund.

BRIEF DESCRIPTION OF THE DRAWINGS

The objects and advantages of the invention will be apparent uponconsideration of the following detailed description, taken inconjunction with the accompanying drawings, in which like referencecharacters refer to like parts throughout, and in which:

FIG. 1 shows an illustrative diagram in accordance with principles ofthe disclosure;

FIG. 2 shows an illustrative flow chart in accordance with principles ofthe disclosure;

FIG. 3 shows another illustrative flow chart in accordance withprinciples of the disclosure; and

FIG. 4 shows yet another illustrative flow chart in accordance withprinciples of the disclosure.

DETAILED DESCRIPTION OF THE DISCLOSURE

Apparatus, methods and systems for detecting non-linear datadependencies in machine learning using hybrid quantum computing isprovided. The system may include a machine learning algorithm operatingon a classical computer. The system may include a quantum algorithmoperating on a hybrid quantum computer. The system may include ahardware processor.

The machine learning algorithm may be operable to receive a data set.The data set may include a plurality of data elements. The machinelearning algorithm may identify the plurality of data elements withinthe data set. The machine learning algorithm may identify one or morefeatures for each data element included in the plurality of dataelements. The machine learning algorithm may determine a total number offeatures for the data set. The machine learning algorithm may input thetotal number of features into a classical machine learning model. Themachine learning algorithm may output, from the classical machinelearning model, a first result set.

The quantum algorithm may receive a selection of an accuracy metric. Thequantum algorithm may receive the data set. The quantum algorithm mayidentify one or more features for each data element included in theplurality of data elements. The quantum algorithm may determine thetotal number of features for the data set. The quantum algorithm mayreduce, by a quantum annealing method, based on the accuracy metric, thetotal number of features to a reduced number of features.

The quantum annealing method may reduce the total number of features toa reduced number of features based on the accuracy metric by reducingfeatures that are correlated over a predetermined threshold ofcorrelation. The quantum annealing method may also reduce the totalnumber of features to a reduced number of features based on the accuracymetric by reducing features that are duplicated over a predeterminedthreshold of duplication.

The accuracy metric may be selected from a scale of 1 to 100. Theaccuracy metric may be selected based on a possibility metric associatedwith the data set. When a possibility of false negative is greater, theaccuracy metric may be set toward a first end. When a possibility offalse positive is greater, the accuracy metric may be set toward asecond end.

The quantum algorithm may input the reduced number of features into aquantum machine learning model. The quantum algorithm may output, fromthe quantum machine learning model, a second result set.

The hardware processor may compare the first result set to the secondresult set. The hardware processor may identify that the result set ascompared to the second result set obtained a result greater than apredetermined threshold degree of similarity.

In some embodiments, the quantum annealing method is executed by aquantum hardware processor operating with a hardware memory. In otherembodiments, the quantum annealing method is executed by a classicaloptimizer operating on a classical hardware processor operating with ahardware memory. In yet other embodiments, the quantum annealing methodis executed by a simulated quantum method executed within a classicalhardware processor operating with a hardware memory.

In certain embodiments, apparatus methods and systems may be used tointerpret human utterances and/or create a chatbot. The apparatusmethods and systems may utilize natural language processing (NLP) andnatural language understanding (NLU). As such, the accuracy metric maypinpoint how accurately the human utterances are interpreted. The dataset elements may include one or more natural language utterances. Eachof the features may include one or more grams. A gram may be a unigram,bigram, trigram or any other suitable gram. A gram may correspond to aword, or any other linguistic component. The reduced number of featuresmay include reduced number of grams. The outputted result from themachine learning model may be used to create a chatbot. The chatbot mayinterpret human utterances, identify responses to the human utterancesand present the identified responses to one or more users.

In some embodiments, apparatus methods and systems may be used torecommend services and/or products to a user. As such, the data set maycorrespond to historical preferences of the user, services and/orproduct history of the user, preferences of a plurality of users andservice and/or product history of the plurality of users. Furthermore,the first result set and the second result set may include one or moreservices and/or products to recommend to the user. The accuracy metricmay pinpoint how appropriately the services and/or products recommendedare suitable for the user. The hardware processor, using a userinterface, may recommend the one or more services and/or products to theuser.

In yet some embodiments, apparatus methods and systems may be used forthree-dimensional image processing. As such, the data set may correspondto text files, image files, audio files and audio/visual files for imageprocessing, human identification and three-dimensional computer sceneunderstanding and interpretation. Furthermore, the one or more featuresmay correspond to components of the text files, image files, audio filesand audio/visual files. The first result set and the second result setmay correspond to a classification of the text files, image files, audiofiles and audio/visual files. The classification may classify the filesindividually or as a combination of files. The accuracy metric maypinpoint how accurately the files are to be classified. A set of imagesmay be processed using the classification of the text files, imagefiles, audio files and audio/visual files individually and theclassification of the combination of text files, image files, audiofiles and audio/visual files.

Apparatus and methods described herein are illustrative. Apparatus andmethods in accordance with this disclosure will now be described inconnection with the figures, which form a part hereof. The figures showillustrative features of apparatus and method steps in accordance withthe principles of this disclosure. It is to be understood that otherembodiments may be utilized and that structural, functional andprocedural modifications may be made without departing from the scopeand spirit of the present disclosure.

The steps of methods may be performed in an order other than the ordershown or described herein. Embodiments may omit steps shown or describedin connection with illustrative methods. Embodiments may include stepsthat are neither shown nor described in connection with illustrativemethods.

Illustrative method steps may be combined. For example, an illustrativemethod may include steps shown in connection with another illustrativemethod.

Apparatus may omit features shown or described in connection withillustrative apparatus. Embodiments may include features that areneither shown nor described in connection with the illustrativeapparatus. Features of illustrative apparatus may be combined. Forexample, an illustrative embodiment may include features shown inconnection with another illustrative embodiment.

FIG. 1 shows an illustrative flow chart. Step 102 shows 10,000,000features to input into an AI/ML model. The features may be retrievedfrom breaking down the components of one or more data sets. However, inorder to process the features without wasting resources and time, it maybe desirable to reduce the number of features to 1,000 features, asshown at 104. Examples of features may be columns, or cells, in a tableor data object.

Once the features have been reduced, the features may be input into anAI/ML model/engine, as shown at 106. The AI/ML model/engine may processthe features and the associated one or more data sets to produce anoutput. The output of the AI/ML model/engine may be presented to theuser, as shown at 108.

FIG. 2 shows an illustrative flow chart. The illustrative flow chartshows the use of a quantum annealing method to reduce the number offeatures, also known as dimensions, of a data set, in order to reducewasted resources and time, while retaining the accuracy level of theoutput. Examples of features may be columns, or cells, in a table ordata object. The accuracy level may be set by the user and/or may beproblem-dependent. As such, the accuracy level may be set based on theidentified possibility of a false positive output vis-à-vis theidentified possibility of a false negative output. As such, criticalapplications may be set to a high accuracy level, while standardapplications may be set to a low accuracy level. When using an accuracyscale of 1-100, an example of a high accuracy level may be 80-100, whilean example of a low accuracy level may be 0-20.

Step 202 shows a large number of features have been culled and areprepared to be input into an AI/ML engine. Step 204 shows reducing thenumber of features using a quantum annealing algorithm. Step 206 showsthat the AI/ML engine receives the reduced number of features. As aresult of receiving the reduced number of features, the AI/ML engine isoptimized. The output of the AI/ML engine is presented to a user, asshown at step 208.

FIG. 3 shows an illustrative diagram. The illustrative diagram shows atwo-dimensional energy landscape that resulted from a reduction oroptimization algorithm. The lowest energy point within energy landscape302 may be shown at A. Molecular dynamics to be used to search for, andidentify, A within the full space of the energy landscape.

A quantum annealer, or quantum annealing algorithm, may also be used toidentify the lowest energy point. As such, at 308, a quantum annealer orquantum annealing algorithm may identify B as the lowest energy point.The quantum annealer may search for, and identify, B precisely in areduced subspace, as shown at 310.

FIGS. 4A and 4B show illustrative diagrams. FIG. 4A showsthree-dimensional energy landscapes 402. FIG. 4B shows athree-dimensional energy landscape 404. Both three-dimensional energylandscapes 402 and 404 may be associated with the result of a reductionor optimization algorithm.

Three-dimensional energy landscape 402 may include various attempts toidentify the lowest energy point. Each attempt, shown as arrows 406,408, 410 and 412 may identify a valley within the energy landscape. Uponidentification of a plurality of valleys, a system may determine whichvalley is the lowest. The lowest valley may correspond to the lowestenergy point. In three-dimensional energy landscape 402, the lowestenergy point may correspond to the valley identified by arrow 410.

Three-dimensional energy landscape 404 may include various attempts toidentify the lowest energy point. Each attempt, shown as arrows 414,416, 418 and 420 may identify the same valley within the energylandscape. As such, the valley identified within the energy landscapemay be the lowest energy point.

It should be noted that the lowest energy point within an energylandscape may correspond to the most efficient, or most optimized,method within a landscape of methods.

Thus, systems and methods for detecting non-linear data dependencies inmachine learning using hybrid quantum computing are provided. Personsskilled in the art will appreciate that the present invention can bepracticed by other than the described embodiments, which are presentedfor purposes of illustration rather than of limitation. The presentinvention is limited only by the claims that follow.

What is claimed is:
 1. A method for using natural language processing(NLP) and natural language understanding (NLU) to interpret humanutterances and create a chatbot, the method involving detectingnon-linear data dependencies using hybrid quantum computing, the methodcomprising: receiving a selection of an accuracy metric for accuratelyinterpreting human utterances; receiving a data set, the data setcomprising a plurality of data elements, said plurality of data elementsincluding one or more natural language utterances, for processing by amachine learning model operating on a machine learning system;identifying the plurality of data elements within each data set;identifying one or more features for each data element included in theplurality of data elements; determining a total number of features forthe data set, each of the features including comprising one or moregrams; reducing, by a quantum annealing method, based on the accuracymetric, the total number of features to a reduced number of features,the reduced number of features comprising a reduced number of grams;inputting the reduced number of features comprising the reduced numberof grams into the machine learning model; outputting a result from themachine learning model; and using the result to create a chatbot; usingthe chatbot to interpret human utterances; using the chatbot to identifyresponses to the human utterances; and using the chatbot to present theidentified responses.
 2. The method of claim 1, wherein the accuracymetric is selected from a scale of 1 to
 100. 3. The method of claim 1,wherein the accuracy metric is selected based on a possibility metricassociated with the data set, wherein when the possibility of falsenegative is greater, the accuracy metric is set toward a first end, whenthe possibility of false positive is greater, the accuracy metric is settoward a second end.
 4. The method of claim 1, wherein the quantumannealing method is executed by a quantum hardware processor operatingwith a hardware memory.
 5. The method of claim 1, wherein the quantumannealing method is executed by a classical optimizer operating on aclassical hardware processor operating with a hardware memory.
 6. Themethod of claim 1, wherein the quantum annealing method is executed by asimulated quantum method executed within a classical hardware processoroperating with a hardware memory.
 7. The method of claim 1, wherein thequantum annealing method reduces the total number of features to areduced number of features based on the accuracy metric by: reducingfeatures that are correlated over a predetermined threshold ofcorrelation; and reducing features that are duplicated over apredetermined threshold of duplication.
 8. A system for recommendingservices and/or products to a user, said system detecting non-lineardata dependency using hybrid quantum computing: a machine learningalgorithm operating on a classical computer, the machine learningalgorithm operable to: receive a data set comprising a plurality of dataelements, said data set corresponding to historical preferences of theuser, service and/or product history of the user, preferences of aplurality of users and service and/or product history of the pluralityof users; identify the plurality of data elements within the data set;identify one or more features for each data element included in theplurality of data elements; determine a total number of features for thedata set; input the total number of features into a classical machinelearning model; and output, from the classical machine learning model, afirst result set, said first result set comprising one or more servicesand/or products to recommend to the user; a quantum algorithm operatingon a hybrid quantum computer, the quantum algorithm operable to: receivea selection of an accuracy metric that the services and/or productsrecommended are appropriate for the user; receive the data set; identifyone or more features for each data element included in the plurality ofdata elements; determine the total number of features for the data set;reduce, by a quantum annealing method, based on the accuracy metric, thetotal number of features to a reduced number of features; input thereduced number of features into a quantum machine learning model; andoutput, from the quantum machine learning model, a second result set,said second result set comprising one or more services and/or productsto recommend to the user; and a hardware processor operable to: comparethe first result set comprising one or more services and/or products torecommend to the user to the second result set comprising one or moreservices and/or products to recommend to the user; identify that thefirst result set as compared to the second result set obtained a resultgreater than a predetermined threshold degree of similarity; andrecommend, using a user interface, the one or more services and/orproducts to the user.
 9. The system of claim 8, wherein the quantumannealing method reduces the total number of features to a reducednumber of features based on the accuracy metric by: reducing featuresthat are correlated over a predetermined threshold of correlation; andreducing features that are duplicated over a predetermined threshold ofduplication.
 10. The system of claim 8, wherein the accuracy metric isselected from a scale of 1 to
 100. 11. The system of claim 8, whereinthe accuracy metric is selected based on a possibility metric associatedwith the data set, wherein when a possibility of false negative isgreater, the accuracy metric is set toward a first end, when apossibility of false positive is greater, the accuracy metric is settoward a second end.
 12. A method for three-dimensional imageprocessing, the method involving non-linear data dependency detectionusing hybrid quantum computing, the method comprising: in a first step:receiving a data set, the data set comprising a plurality of dataelements, said data set corresponding to text files, image files, audiofiles and audio/visual files for image processing, human identificationand three-dimensional computer scene understanding and interpretation,for processing by a machine learning model operating on a machinelearning system; identifying the plurality of data elements within thedata set; identifying one or more features for each data elementincluded in the plurality of data elements, the one or more featurescorresponding to components of the text files, image files, audio filesand audio/visual files; determining a total number of features for thedata set; inputting the total number of features into the machinelearning model; and outputting a first result set from the machinelearning model, the first result set corresponding to classification ofthe text files, image files, audio files and audio/visual filesindividually and as a combination of text files, image files, audiofiles and audio/visual files; in a second step: receiving a selection ofan accuracy metric, said accuracy metric for pinpointing a degree ofaccuracy of the classification of the text files, image files, audiofiles and audio/visual files; receiving the data set, for processing bythe machine learning model operating on the machine learning system;identifying the plurality of data elements within the data set;identifying one or more features for each data element included in theplurality of data elements; determining the total number of features forthe data set; reducing, by a quantum annealing method, based on theaccuracy metric, the total number of features to a reduced number offeatures; inputting the reduced number of features into the machinelearning model; and outputting a second result set from the machinelearning model, the second result set corresponding to classification ofthe text files, image files, audio files and audio/visual filesindividually and as the combination of text files, image files, audiofiles and audio/visual files; in a third step: comparing the firstresult set to the second result set; identifying that the first resultset as compared to the second result set obtained a result greater thana predetermined threshold degree of similarity; and processing a set ofimages using the classification of the text files, image files, audiofiles and audio/visual files individually and the classification of thecombination of text files, image files, audio files and audio/visualfiles.
 13. The method of claim 12, wherein the accuracy metric isselected from a scale of 1 to
 100. 14. The method of claim 12, whereinthe accuracy metric is selected based on a possibility metric associatedwith the data set, wherein when the possibility of false negative isgreater, the accuracy metric is set toward a first end, when thepossibility of false positive is greater, the accuracy metric is settoward a second end.
 15. The method of claim 12, wherein the quantumannealing method is executed by a quantum hardware processor operatingwith a hardware memory.
 16. The method of claim 12, wherein the quantumannealing method is executed by a classical optimizer operating on aclassical hardware processor operating with a hardware memory.
 17. Themethod of claim 12, wherein the quantum annealing method is executed bya simulated quantum method executed within a classical hardwareprocessor operating with a hardware memory.
 18. The method of claim 12,wherein the quantum annealing method reduces the total number offeatures to a reduced number of features based on the accuracy metricby: reducing features that are correlated over a predetermined thresholdof correlation; and reducing features that are duplicated over apredetermined threshold of duplication.
 19. A hybrid computing methodthat utilizes a classical computer operating with a graphical processingunit (GPU) and a quantum optimizer to build a model that efficientlyperforms hyper parameter optimization, the method comprising: receiving,at a dependency detection subsystem operating on the classical computer,a set of input parameters and a set of current values assigned to eachinput parameter included in the set of input parameters, said set ofinput parameters and set of current values relating to a predeterminedclassification structure; transferring the set of input parameters andthe set of current values assigned to each input parameter to thequantum optimizer executing a quantum annealing method; identifying, atthe quantum optimizer, a set of hyperparameters included in the set ofinput parameters; reducing, at the quantum optimizer, the set ofhyperparameters at the quantum optimizer using the quantum annealingmethod; returning the reduced set hyperparameters from the quantumoptimizer to the classical computer; and building, at the classicalcomputer operating with the GPU, a machine learning model using thereduced set of hyperparameters.
 20. The method of claim 19, furthercomprising using the machine learning model operating on the classicalcomputer to classify an unclassified data element within thepredetermined classification structure.
 21. The method of claim 20,wherein the unclassified data element is an email, and the predeterminedclassification structure classifies the email as being a valid email ora malicious email.